library(tidyverse)
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## ✔ purrr 1.0.2
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library(tayloRswift)
library(tidytuesdayR)
library(ggplot2)
library(dplyr)
library(ggthemes)
library(maps)
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## map
library(sf)
## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE
library(cartography)
## This project is in maintenance mode.
## Core functionalities of `cartography` can be found in `mapsf`.
## https://riatelab.github.io/mapsf/
library(plotly)
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## last_plot
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## layout
shapenames <- read_sf( "CA_Counties", "CA_Counties_TIGER2016")
jail_America <- readr::read_csv("./Allstatesinsurvey/all_jails.csv")
## Rows: 523 Columns: 116
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (18): statecode, state, state_notes, county, jail, jail_notes, med2008, ...
## dbl (98): id, fips, d2008, d2009, d2010, d2011, d2012, d2013, d2014, d2015, ...
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jail_deaths_in_Americas <- readr::read_csv("./Allstatesinsurvey/all_deaths.csv")
## Rows: 7571 Columns: 22
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## Delimiter: ","
## chr (18): state, county, jail, date_of_death, full_name, last_name, first_na...
## dbl (4): id, year, yob, age
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#irports_Jordan %>%
#leaflet() %>%
#addTiles() %>%
#addMarkers(lng = ~longitude, lat = ~latitude, label = ~description)
#calif
California_deaths <-jail_deaths_in_Americas %>%
filter(state=="CA")%>%
group_by(county)%>%
summarize( total_deaths=n()) #counts how many rows inside that group
prisondeath<-shapenames %>%
left_join( California_deaths, by=c( "NAME"= "county"))
map <- ggplot(data= prisondeath, aes(fill= total_deaths)) +
geom_sf(fill="white")+
labs(x="Latitude of California", y="Lonigitude of California")+
geom_sf_text(mapping = aes(label= NAME), size=1)+
theme(legend.position = "none")
ggplotly(map)